Either a single mass spectrum or acquisition of multipe mass spectra over short timespan.

Sample measurement leads to a recognisable ‘ambient’ peak. Mass spectra before and after are mainly noise from chemical and instrumental origin.

Mass peaks of biological origin within the ‘ambient’ peak. However, large variability across scans, negatively influencing reproducibility.

Mass peaks of biological origin within the ‘ambient’ peak. However, large variability across scans, negatively influencing reproducibility.

• Single spectrum-based
• Across (part of) the ambient peak

• Single spectrum-based
• Across (part of) the ambient peak

• Single spectrum-based
• Across (part of) the ambient peak

• Single spectrum-based
• Across (part of) the ambient peak

• Identification of region of interest (single scan or across peak)
• No chromatographic separation: feature identification impractical
• Mass deviation shifts (intra- and inter- batch differences)
• Lock mass correction (can influence ionisation efficiency)
• Feature correspondence between batches and in real-time setting

sps <- raw %>% filterFile(3) %>% spectra
sps.binned <- Spectra::bin(sps[which.max(tic(sps))],binSize=0.01)
peaks <- sps.binned %>% peaksData

1. No distinction between biological and noise mass peaks
2. Feature information and accurate masses are lost
3. Feature filtering on minimal intensity and CV

• Waters instruments: Progenesis
• Agilent instruments: MassHunter

LA-REIMS data: waters .RAW files with Progenesis QI

• Developed for LC-MS applications
• Option for direct sample analysis, likely LC-MS approach to peak picking

• Black box approach
• Accurate masses distorted after batch processing
• Little to no user control on peak picking

Peak detection using cwt algorithm on centroided data.

cwp <- CentWaveParam(peakwidth=c(2,8), noise=100, ppm=50, snthresh=0.5, mzdiff=0.05, 
                     fitgauss=TRUE, extendLengthMSW=TRUE, firstBaselineCheck=FALSE, 
                     prefilter=c(1,100))

chr_1 <- findChromPeaks(chr_1, cwp)
chromPeaks(chr_1)

cwp <- CentWaveParam(peakwidth=c(2,8), noise=100, ppm=50, snthresh=0.5, mzdiff=0.05, 
                     fitgauss=TRUE, extendLengthMSW=TRUE, firstBaselineCheck=FALSE, 
                     prefilter=c(1,100))

chr_1 <- findChromPeaks(chr_1, cwp)
chromPeaks(chr_1)

‘Chromatographic peaks’ are not consistently identified, despite good mass peak shape in individual mass spectra.

chr_3 <- findChromPeaks(chr_3, cwp)
chromPeaks(chr_3)

Peak detection on the four files yields a total of only 2620 mass peaks.

cwt <- findChromPeaks(cwt, param=cwp)
peaks <- as.data.frame(chromPeaks(cwt))

• Distribution of peaks detected very skewed
• Majority is detected in only two samples

table(peaks$sample)

##    1    2    3    4 
##  489 1005  823  303

## Perform the correspondence using fixed m/z bin sizes.
pdp <- PeakDensityParam(sampleGroups = sampleData(cwt)$sample_group,
                        minFraction = 0.4, bw = 30)
cwt <- groupChromPeaks(cwt, param = pdp)

featureDefinitions(cwt) |> head()

The majority of features has 2 to 4 ‘chromatographic’ peaks assigned.

table(featureDefinitions(cwt)$npeaks)

##   2   3   4   5   6 
## 298 169 157  22   9

A minority of features is defined by more than 4 mass peaks.

A minority of features is defined by more than 4 mass peaks.

msw <- MSWParam(scales = c(0.1,0.2,0.4,0.8,1,2,4,8), nearbyPeak = TRUE, winSize.noise = 
                500, SNR.method = "data.mean", snthresh = 2, ampTh = 0.00005, 
                peakScaleRange = 2, ridgeLength=24)

raw <- findChromPeaks(single, param=msw)
peaks <- as.data.frame(chromPeaks(raw))

Mass peaks are more uniformly detected in all samples.

A total of 2931 features were found in the spectra.

prm <- MzClustParam(sampleGroups = sampleData(raw)$sample_group)
raw <- groupChromPeaks(raw, param = prm)

features <- featureDefinitions(raw)

table(features$npeaks)

##    2    3    4 
##  558  593 1780

When removing the chromatographic peaks for which no S/N could be calculated, only 1212 features are found.

##   2   3   4 
## 367 366 479

• Multiple false hits in case of large ppm windows
• Highly accurate exact mass required

• Multiple false hits in case of large ppm windows
• Highly accurate exact mass required

• Multiple false hits in case of large ppm windows
• Highly accurate exact mass required

Whether or not a feature is biological or not can be judged on peak shape:

1. Region of interest identification: wavelet, similarity, tic, mass peaks
2. Lock mass-free alignment procedure (inter- and intra- batch)
3. Feature quantification
4. Compound annotation (adducts and isotopes)

1. Region of interest identification: wavelet, similarity, tic, mass peaks
2. Lock mass-free alignment procedure (inter- and intra- batch)
3. Feature quantification
4. Compound annotation (adducts and isotopes)

• Define spectral representation points for each distinct mass peak

• Group mass peaks within allowable mass window of the mass peak

• Group mass peaks within allowable mass window of the mass peak

• Group mass peaks within allowable mass window of the mass peak

• Group mass peaks within allowable mass window of the mass peak

• Inter-batch mass differences need consideration prior to alignment

• Both centroided and raw spectral are required (mass alignment, feature annotation, …)

input <- 'E:/UGent_LIMET/02_Mass_spectrometry/raw_files/saliva_demo'
path_msconvert <- 'C:/Program Files/ProteoWizard/ProteoWizard 3.0.24045.2c2c542/
msconvert.exe'

raw <- 'E:/UGent_LIMET/02_Mass_spectrometry/mzML_files/raw/saliva_demo'
centroided <- 'E:/UGent_LIMET/02_Mass_spectrometry/mzML_files/centroided/saliva_demo'

msconvert(input, centroided, path_msconvert, processed='cwt', mz=0.025, snr=2.0, 
          filter='absolute', orientation='most-intense', threshold=200, dir=TRUE,
          verbose=TRUE)
msconvert(input, raw, path_msconvert, processed='none', dir=TRUE, verbose=TRUE)

• Identification of ‘hook’ points consistently present in all spectra
• Select one or few similar spectra consistent in peak composition (> 20 samples)

cent <- readMsExperiment(spectraFiles = fls)
indcs <- scan_selection(cent, method='tic', write=TRUE)

• Determine allowable mass window for correspondence
• Determine spectral representations for all features

• Correct any mass discrepancies between samples or larger cohorts

deviation <- calculate_mz_shift(cent, refPoints=ref_pts, indcs=indcs, 
                                shift='loess', dev=c(-150,150))
centroided <- correct_mz_drift(cent, factor=deviation)

• Centroided peaks might fall outside of allowable mass window
• Apply spectrum-specific correction based on hook points

• Creation of composite spectrum yields better estimates of peak boundaries and of the accurate masses of each features

composite <- composite_spectrum(raw, indcs, algnPoints=algn_pts, combine='avg', 
                                normalise1='is', normalise2='tic') 

• Region of interest
  • Single scan (ie. highest summed intensity scan)
  • Across (part of) the ambient peak
• Mass peak signal:
  • Centroided intensity
  • Integrated peak area

• Compound identification only possible with exact mass
• Lock mass calibration necessary (eg. matrix-specific component)

composite <- recalibrate_masses(composite=composite, lockmass=215.0327891, 
                                snr=1, q_score=0.5, ppm=3.4e-5)

1. Consider ease of use, sensitivity and selectivity criteria when choosing appropriate data pre-processing methodology
2. Be aware of inter- and intra-batch mass deviations
3. Highly accurate estimate of exact mass needed for putative annotation
   • Supported by adducts and isotopic species
   • Feature filtering
4. Scan selection and peak integration affect reproducibility of feature abundances
5. Look at your raw data and play with it!